Evidence map›Paper›PMID 29149912›Full record

Trial reportTrials2017

Trial to Incentivise Adherence for Diabetes (TRIAD): study protocol for a randomised controlled trial.

Marcel Bilger, Mitesh Shah, Ngiap Chuan Tan, Kaye Louise Howard, Hui Yan Xu, Ecosse Luc Lamoureux, Eric Andrew Finkelstein

Registry-linked trialAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Trials, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02224417 (A Randomized Controlled Trial to Improve Diabetes Outcomes Through Financial Incentives), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT02224417 nacompletednot on this map

A Randomized Controlled Trial to Improve Diabetes Outcomes Through Financial Incentives

TypeinterventionalSponsorDuke-NUS Graduate Medical SchoolRan2015 to 2018Enrolled240ConditionsType 2 Diabetes MellitusArmsDiabetes Educational Program (DEP), DEP + Process Incentive, DEP + Outcome Incentive
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Marcel BilgerHealth Services and Systems Research, Duke-NUS Medical School, 8 College Road, 169857, Singapore, Singapore. marcel.bilger@duke-nus.edu.sg.ORCID http://orcid.org/0000-0002-2872-6597
Mitesh ShahSingHealth Polyclinics, Singapore, Singapore.
Ngiap Chuan TanSingHealth Polyclinics, Singapore, Singapore.
Kaye Louise HowardHealth Services and Systems Research, Duke-NUS Medical School, 8 College Road, 169857, Singapore, Singapore.
Hui Yan XuSingHealth Polyclinics, Singapore, Singapore.
Ecosse Luc LamoureuxSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Eric Andrew FinkelsteinHealth Services and Systems Research, Duke-NUS Medical School, 8 College Road, 169857, Singapore, Singapore.

Funding

National University of Singapore Global Asia Institute NIHA-2013-1-005
6 · The paper itself

Abstract

backgroundMany people with diabetes have suboptimal glycaemic control due to not being adherent to their treatment regimen. Behavioural economic theory suggests that the lack of adherence results from the disconnect between the timing of when costs and benefits accrue. One strategy to address this discontinuity is to offer patients a near-term benefit, such as a financial reward. Whereas there is evidence that rewards can improve treatment adherence and sometimes health outcomes, further research is needed to determine whether rewards are more effective when targeting processes or intermediary health outcomes. In the Trial to Incentivise Adherence for Diabetes (TRIAD) we test whether adding financial incentives to usual care can improve HbA1c levels among people with diabetes and whether the financial incentives work better when targeting processes (adherence to blood glucose testing, medication, and daily physical activity) or the primary intermediary health outcome of self-monitored blood glucose within an acceptable range. METHODS/

designTRIAD is a randomised, controlled, open-label, single-centre superiority trial with three parallel arms. A total of 240 patients with suboptimally controlled diabetes (HbA1c ≥ 8%) from a polyclinic in Singapore are block-randomised (blocking factor: current vs. new glucometer users) into three arms, namely (1) usual care (UC) only, (2) UC with process incentive and (3) UC with outcome incentive, in a 2:3:3 ratio. Masking the arm allocation will be precluded by the behavioural nature of the intervention but blocking size will not be disclosed to protect concealment. The primary outcome (change in HbA1c level at month 6) will be measured by a laboratory that is independent from the study team. Secondary outcomes (at month 6) include the number of blood glucose testing days, glucose readings within the normal range (between 4 to 7 mmol/L), medication-adherent days, physically active days, and average incentives earned and time spent administrating the incentives. DISCUSSION: This study will provide evidence on whether financial incentives can cost-effectively improve glycaemic control. It will also provide evidence on the benefit incidence of interventions involving financial incentives. By comparing process to outcome incentives, this study will inform the design of future incentive strategies in chronic disease management and beyond.

trial registrationClinicalTrials.gov registry, ID: NCT02224417 . Registered on 22 August 2014.

Indexed as

Clinical ProtocolsTreatment Adherence and ComplianceBlood GlucoseDiabetes MellitusGlycated HemoglobinHumansMotivationOutcome Assessment, Health CareSample SizeBlood GlucoseGlycated Hemoglobinhemoglobin A1c protein, humanBehaviour changeBlood glucose monitoringDiabetesFinancial incentiveMedication adherencePhysical activity

Identifiers

PMID29149912
PMCPMC5693491

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LicenceCC BY
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.